Microbial Profiling using Mothur pipeline

Mothur Snakemake workflow

A tentative visualization of the snakemake workflow that defines mothur bioinformatics rules in a DAG (directed acyclic graph) format. A detailed interactive snakemake report is available here.


Getting started

Create a mothur YAML file

name: mothur
channels:
    - conda-forge
    - bioconda
    - defaults
dependencies:
    - mothur =1.48.0
    - vsearch =2.22.1

Create mothur env using the YAML file

conda activate base
conda env create -n mothur48 --file mothur48.yml
conda activate mothur48

Download references databases

  • Download Silva alignment reference database.
  • Process degapped Silva classifier from Silva alignments.
  • Download RDP classifier.

Example: Downloading mothur references using mothurReferences.sh script on CLI.

bash workflow/scripts/mothurReferences.sh


Mothur classification methods

There are four methods that can be used to profile microbial communities present in a sample. Here we briefly decribe each method:

1.Classify OTUs

  • OTUs (Operational Taxonomic Units (OTUs)) are clusters of similar sequences and are commonly accepted as analytical units in microbial profiling when using 16S rRNA gene markers.

2. Classify Phylotypes

  • A phylotype in microbiome research is a DNA sequence or group of sequences sharing more than an arbitrarily chosen level of similarity of a 16S rRNA gene marker.

3. Classify ASVs

  • ASVs Amplicon Sequence Variants (ASVs)in microbiome research is any inferred single DNA sequences recovered from a bioinformatics analysis of 16S rRNA marker genes.
  • ASV is typically really a cluster of sequences that are one or two bases apart from each other.

4. Classify Phylogenies

  • Microbial phylogenies are from gene sequence homologies. Models of mutation determine the most-likely evolutionary histories.


Preliminary OTU analysis using Mothur

The preliminary analysis (alpha_beta_diversity rule) is part of the bioinformatics analysis. It includes:

  • Creating reads count for each group.
  • Subsampling for downstream analysis.
  • Rarefaction.
  • Computing Alpha diversity metrics.
  • Computing Beta diversity metrics.
  • Getting sample distances.
  • Constructing sample phylip tree.
  • Generating ordination matrices including PCoA and NMDS.


References

[1]
Buza, T. M., Tonui, T., Stomeo, F., Tiambo, C., Katani, R., Schilling, M., … Kapur, V. (2019). iMAP: An integrated bioinformatics and visualization pipeline for microbiome data analysis. BMC Bioinformatics, 20. https://doi.org/10.1186/S12859-019-2965-4


Appendix

Project main tree

.
├── LICENSE.md
├── README.md
├── REFER
│   ├── dags
│   ├── data
│   ├── envs
│   ├── images
│   ├── imap-bioinformatics.Rproj
│   ├── index.html
│   ├── render.R
│   ├── report.html
│   ├── resources
│   ├── results
│   ├── rules_dag.sh
│   ├── smk_html_report.sh
│   └── workflow
├── config
│   ├── config.yml
│   ├── pbs-torque
│   ├── samples.tsv
│   ├── slurm
│   └── units.tsv
├── dags
│   ├── rulegraph.png
│   └── rulegraph.svg
├── data
│   ├── README.md
│   ├── metadata
│   ├── mothur
│   ├── qiime
│   ├── reads
│   ├── references
│   └── test
├── images
│   ├── bioinformatics.png
│   ├── bkgd.png
│   └── smkreport
├── index.Rmd
├── library
│   ├── apa.csl
│   ├── export.bib
│   ├── imap.bib
│   └── references.bib
├── mothur.1679202274.logfile
├── mothur.1679202878.logfile
├── report.html
├── results
│   └── project_tree.txt
├── styles.css
├── tree.sh
└── workflow
    ├── Snakefile
    ├── envs
    ├── rules
    └── scripts

27 directories, 28 files

Screenshot of interactive snakemake report

The interactive snakemake HTML report can be viewed by opening the report.html using any compatible browser. You will be able to explore the workflow and the associated statistics. You can close the left bar to get a more expansive display view.

Troubleshooting of FAQs

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      2. Answer
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      3. Answer